Machine Learning Guided Prediction of Superhard Materials Based on Compositional Features

Abstract

In this study, the mechanical properties of materials were predicted using machine learning to search for superhard materials. Based on an AFOW database consisting of DFT quantum calculation values, the mechanical properties of materials were predicted using various machine learning models. For supervised learning, the entire data was divided into training data and test data at a ratio of 8:2. Since the discovery of superhard materials can be predicted based on the bulk modulus and shear modulus, the bulk modulus was primarily predicted using only the chemical compositional ratio (chemical formula), and then the shear modulus was obtained using the predicted bulk moduli. To obtain good prediction performance, cross-validation and hyper-parameter tuning were carried out. Each characteristic was predicted using XGBoost, one of the ensemble algorithms, and its performance was compared to the treebased machine learning of RandomForest and Support Vector Machine regression using the coefficient of determination (R²) and root-mean-square-error (RMSE) as metrics. For the recently introduced four superhard materials (Mo0.9W1.1BC, ReWC0.8, MoWC2, and ReWB), the results of this study were similar to those of previous studies including the experimental values or the DFT quantum calculations. The shear modulus was underpredicted, which can be understood since structural properties were not considering as a feature in our machine learning models.